Invoice Processing Research Articles

Artificial Intelligence (AI) in working capital management: Practices and future potential

This study delves into how artificial intelligence (AI) transforms working capital management by addressing the limitations of traditional methods. The focus is to critically review research publications, case studies and industry reports using qualitative research methodology to examine how AI improves operational efficiency and decision-making in this area. The study demonstrates the practical application of advanced machine learning algorithms and big data analytics in optimizing inventory management, enhancing demand forecasting and improving cash flow predictions. A thorough review of recent research and case studies reveals additional benefits, including automated reconciliations, debtor risk analysis, accelerated cash inflows, invoice processing and proactive working capital management. Despite challenges in integrating AI with legacy systems, the potential for substantial improvements in financial health and operational efficiency is significant. The study also suggests future research directions, such as developing comprehensive AI-driven applications for broader working capital considerations, creating empirical validation frameworks for model performance and addressing ethical considerations to fully harness AI's potential in optimizing working capital management.

Transforming accounting practices: The impact and challenges of business intelligence integration in invoice processing

Transforming accounting practices: The impact and challenges of business intelligence integration in invoice processing

USE OF ARTIFICIAL INTELLIGENCE IN ACCOUNTING AND ANALYTICAL PROCESSES

The beginning of a new era has led to the emergence of new factors that impede the effective operation of enterprises. One of these factors is the exponential growth of the amount of data that needs to be processed and analyzed in order to make effective management decisions. This leads to the need to develop or search for a software product aimed at collecting, analyzing and systematizing a large amount of data, as well as performing relevant tasks based on them that artificial intelligence is capable of. The article is aimed at researching and revealing the potential of using artificial intelligence (AI) in the field of accounting. The main focus is on improving the efficiency of the enterprise, which is achieved by automating and optimizing various aspects of accounting work. On the basis of a thorough analysis of the works of foreign and domestic specialists, the theoretical aspects of the concept of artificial intelligence are distinguished on the basis of systemic, functional and information approaches. The directions of compliance of the accountant, which can be artificial intelligence, are identified, namely: processing of invoices, cost management, analysis of financial statements, management of payables and receivables, compliance with tax legislation and detection of fraud and risk management. Ways of using AI in processes of automation and optimization of processes, such as invoice processing, cost management, financial reporting analysis, control of accounts payable and receivable, ensuring compliance with tax legislation and fraud detection and risk management, have been identified. The artificial intelligence technologies for their purpose, which are the most used in the world, are presented. The sequence of actions in the identified needs in artificial intelligence and its outstanding volume is structured. The results of the study confirm the potential of AI to automate operations, increase productivity and accuracy in accounting, as well as reduce risks and improve audit efficiency. Key words: Artificial intelligence, accounting, analysis, accounting automation, accounting information systems, information systematization, management efficiency.

INVOICE DATA EXTRACTION USING OCR TECHNIQUE

Traditional invoice processing involves manual entry of data, leading to human errors, delays,and increased operational costs. The lack of automation results in inefficiencies, hindering organizations from promptly accessing critical financial information. This research addresses the pressing need for a reliable OCR-based solution to automate invoice data extraction, ultimately improving accuracy, reducing processing time, and enhancing overall business productivity. The project aims to automate invoice data extraction through Optical Character Recognition (OCR) techniques. Leveraging advanced image processing and machine learning, the system will analyze scanned or photographed invoices, extracting relevant information such as vendor details, itemized costs, and dates.This automation streamlines manual data entry processes, enhancing accuracy and efficiency in managing financial records. OCR invoicing is the process of training a template-based OCR model for specific invoice layouts, setting up input paths for these invoices, extracting data, and integrating the extracted data with a structured database. Keywords: Invoice, OCR, YOLO algorithm, Data Extraction, Image Processing, Database Integration.

Invoice Processing Using Robotic Process Automation

Invoice Processing Using Robotic Process Automation

Automated invoice processing: Machine learning-based information extraction for long tail suppliers

Automation of incoming invoices processing promises to yield vast efficiency improvements in accounting. Until a universal adoption of fully electronic invoice exchange formats has been achieved, machine learning can help bridge the adoption gaps in electronic invoicing by extracting structured information from unstructured invoice formats. Machine learning especially helps the processing of invoices of suppliers who only send invoices infrequently, as the models are able to capture the semantic and visual cues of invoices and generalize them to previously unknown invoice layouts. Since the population of invoices in many companies is skewed toward a few frequent suppliers and their layouts, this research examines the effects of training data taken from such populations on the predictive quality of different machine-learning approaches for the extraction of information from invoices. Comparing the different approaches, we find that they are affected to varying degrees by skewed layout populations: The accuracy gap between in-sample and out-of-sample layouts is much higher in the Chargrid and random forest models than in the LayoutLM transformer model, which also exhibits the best overall predictive quality. To arrive at this finding, we designed and implemented a research pipeline that pays special attention to the distribution of layouts in the splitting of data and the evaluation of the models.

Automated invoice data extraction using image processing

Manually processing invoices which are in the form of scanned photocopies is a time-consuming process. There is a need to automate the task of extraction of data from the invoices with a similar format. In this paper we investigate and analyse various techniques of image processing and text extraction to improve the results of the optical character recognition (OCR) engine, which is applied to extract the text from the invoice. This paper also proposes the design and implementation of a web enabled invoice processing system (IPS). The IPS consists of an annotation tool and an extraction tool. The annotation tool is used to mark the fields of interest in the invoice which are to be extracted. The extraction tool makes use of opensource computer vision library (OpenCV) algorithms to detect text. The proposed system was tested on more than 25 types of invoices with the average accuracy score lying between 85% and 95%. Finally, to provide ease of use, a web application is developed which also presents the results in a structured format. The entire system is designed so as to provide flexibility and automate the process of extracting details of interest from the invoices.

Adopting information systems at work: a longitudinal examination of trust dynamics, antecedents, and outcomes

ABSTRACT For users to adopt information systems, they must develop trust in such systems. Even though trust theories consistently define trust as dynamic, the development of trust over time has received little empirical attention. The present study examined the development of trust in a newly introduced information system and its association with antecedents related to the individual (e.g. disposition to trust), the information system (e.g. reliability), and the context (e.g. support) at different time points. We further assessed users’ reliance, performance, and well-being as outcomes of trust. Employees (N = 313) of a German public university assessed a newly introduced invoice processing system on four occasions (before system launch, after initial use, five months after launch, ten months after launch). Results from latent growth curve modelling show a non-linear increase of trust in the information system over time with changing predictors: Person factors were stronger predictors of trust in early phases, whereas system characteristics were stronger predictors later in the process. Moreover, users’ trust in the information system correlated positively with reliance, performance, and well-being. Our results highlight the central role of trust for the successful adoption of information systems at work, and offer specific suggestions for their building and maintenance.

Application of Embedded Voice And Digital Forensics System In Financial Cost Management In Biotech Companies.

Financial cost management is a critical component of business operations in the biotech industry. Embedded voice and digital forensics systems are emerging technologies that can improve the efficiency and effectiveness of financial cost management. This abstract explores the application of embedded voice and digital forensics systems in financial cost management in biotech companies. The study highlights the potential of these technologies to enable real-time monitoring and analysis of financial transactions, enhance fraud detection and prevention, and optimize financial cost management. The study reviews the various applications of embedded voice and digital forensics systems in biotech companies, such as invoice processing, expense tracking, and vendor management. The study examines the benefits of using these technologies in financial cost management, such as improved accuracy and efficiency, reduced costs, and enhanced data security. The study also discusses the challenges of implementing embedded voice and digital forensics systems in biotech companies, such as data integration, system interoperability, and organizational resistance to change. The study concludes that embedded voice and digital forensics systems are valuable tools for financial cost management in biotech companies, enabling real-time monitoring and analysis of financial transactions, enhancing fraud detection and prevention, and optimizing financial cost management. The integration of these technologies in biotech companies can improve operational efficiency, reduce costs, and enhance data security, ultimately contributing to the growth and success of the biotech industry.

AUTOMATED INVOICE PROCESSING SYSTEM BASED ON RPA TECHNOLOGIES

The options for improving invoice processing using RPA (Robotic Process Automation) technology are covered in this article. The significance of the subject and the goal of the study are explained in the introduction. The study's major goal is to organize and analyze how robotic invoice processing (RPA) affects the invoice processing process. In the context of streamlining financial procedures, businesses today struggle with the issue of automated invoice processing (RPA). The study looks at this technology's application to processing invoices. The study's objective was to gather and examine data regarding the advantages and possibilities of employing robotic invoice processing (RPA) to streamline invoice processing. We paid specific attention to factors like improved accuracy and error avoidance, resource optimization and cost reduction, compliance and fraud risk reduction, greater profitability, and technology interoperability. The article's primary goals are to examine the fundamental concepts and capabilities of robotic process automation (RPA) in the context of processing invoices; to research the approaches for implementing RPA in this process and evaluate how it affects efficiency and accuracy of operations; to think about potential future applications for RPA in this field; and to identify potential trends in the advancement of this technology. These goals are designed to offer a complete investigation and evaluation of robotic process automation (RPA) technology with regard to its influence on how it improves invoice processing in commercial environments. One of the techniques utilized in the article is to examine the advantages and power of RPA in the context of processing invoices. The investigation' findings demonstrate that RPA technology has greatly enhanced invoice processing. Routine procedures can be automated with RPA, which lowers risks and boosts productivity. This technology is providing new chances for businesses across numerous industries. Overall, the study demonstrates that robotic invoice processing (RPA) is a useful tool for improving the speed and accuracy of financial transactions, optimizing the processing of invoices, and helping the creation of business plans in the current fast-paced corporate environment.

AI Driven Accounts Payable Transformation

Accounts Payable (AP) is a resource-intensive business process in large enterprises for paying vendors within contractual payment deadlines for goods and services procured from them. There are multiple verifications before payment to the supplier/vendor. After the validations, the invoice flows through several steps such as vendor identification, line-item matching for Purchase order (PO) based invoices, Accounting Code identification for Non- Purchase order (Non-PO) based invoices, tax code identification, etc. Currently, each of these steps is mostly manual and cumbersome making it labor-intensive, error-prone, and requiring constant training of agents. Automatically processing these invoices for payment without any manual intervention is quite difficult. To tackle this challenge, we have developed an automated end-to-end invoice processing system using AI-based modules for multiple steps of the invoice processing pipeline. It can be configured to an individual client’s requirements with minimal effort. Currently, the system is deployed in production for two clients. It has successfully processed around ~80k invoices out of which 76% invoices were processed with low or no manual intervention.

Hierarchical classification for account code suggestion

As part of invoice processing, businesses are required to manually classify each line item on an invoice to a specific financial account. This can be time-consuming and challenging when there is a large set of account codes to choose from. Failing to select the correct account will lead to errors in financial reporting which can be misleading for stakeholders and have adverse effects during a financial audit. The emergence of cloud-based accounting platforms has introduced potential areas for automated support across invoice processing tasks such as helping bookkeepers to select the correct financial account. Traditionally, account code suggestion is framed as a multi-class classification task, however, we explore the applicability of using a hierarchical single-label classifier. Account codes can be expressed as taxonomic classes, either explicitly through the chart of accounts or implicitly through associations with reconciled bank accounts. Our research provides evidence to support that the exploitation of hierarchical information from induced taxonomies can be highly advantageous for improving classification and recommendation performance. Furthermore, we introduce Top-K Parent Boosting, a novel post-processing strategy. We demonstrate the suitability of Top-K Parent Boosting for DAG structures and highlight its superiority in improving recommendation performance over former strategies.

End to End Invoice Processing Application Based on Key Fields Extraction

In this paper, an automatic invoice processing system, which is in great demand among private and public companies, was proposed. The proposed system supports all invoice file types that can be submitted by companies. Companies can easily submit invoices to the system via the web interface or email, and all invoices submitted to the system are queued and processed sequentially. If the invoice is a text file, the invoice information is extracted from the text by using template matching. If the invoice is an image, the text and table areas are detected and extracted. For table detection, we used both image processing based and YOLOv5-based deep learning method. Cell extraction was then performed from the extracted table images. As a result of these processes, all text and table cells were obtained as images and these images were converted into machine-readable text using the open-source software Tesseract OCR. Tesseract already provides trained models for English and Turkish. However, these models do not provide successful results for invoices submitted by companies in Turkish. Therefore, the new fine-tuned model trained with invoices in Turkish was used for OCR. The experimental results showed that the trained Turkish model was more accurate than the Turkish and English models provided by Tesseract. In addition, the YOLOv5-based table detection model was more accurate than the image-processing-based table detection method.

Tool for Automated Tax Coding of Invoices

Accounts payable refer to the practice where organizations procure goods and services on credit which need to be reimbursed to the vendors in due time. Once the vendor raises an invoice, it undergoes through a complex process before the final payment. In this process, tax code determination is one of the most challenging steps, which determines the tax to be levied and directly influences the amount payable to a vendor. This step is also very important from a regulatory compliance standpoint. However, it is error-prone, labor (resource) intensive, and needs regular training of the resources as it is done manually. Further, an error in the tax code determination induces penalties on the organization. Automatically arriving at a tax-code for a given product accurately and efficiently is a daunting task. To address this problem, we present an automated end-to-end system for tax code determination which can either be used as a standalone application or can be integrated into an existing invoice processing workflow. The proposed system determines the most relevant tax code for an invoice using attributes such as item description, vendor details, shipping and delivery location. The system has been deployed in production for a multinational consumer goods company for more than 6 months. It has already processed more than 22k items with an accuracy of more than 94% and high confidence prediction accuracy of around 99.54%. Using this system, approximately 73% of all the invoices require no human intervention.

Educational Training For Processing Invoice Of Vendor Identification And Payments Using Python-Tesseract

The aim of the project is to recognize the invoices of receipts from various vendors, by using automated invoice processing using various learning educational tools. This automated invoice processing is far better than manual invoice processing, it saves a serious amount of time and money creating efficiencies and increasing the accuracy of captured data. Basically, the invoices were calculated from the scanned receipts by using python-tesseract software. Python- tesseract is an optical character recognition (OCR) tool for python. It will recognize and read the text embedded in images. So, this python-tesseract software extracts key information like bill or invoice number, amount etc.; from all receipts and imports the calculated invoices and total amount of all receipts which are given by vendors to the database.

Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders. Managing and analyzing unstructured data is a costly affair for the organization. Unstructured data has a wealth of hidden valuable information. Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization. Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents. Researchers have used different approaches for extracting key fields, but the lack of annotated and high-quality datasets is the biggest challenge. Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents. Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality. This work highlights the detailed process flow for end-to-end key fields extraction from unstructured documents. This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique. The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents. The proposed multi-layout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF. We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset. We attained the best results with BiLSTM-CRF model.

How Walmart Canada Used Blockchain Technology to Reimagine Freight Invoice Processing

How Walmart Canada Used Blockchain Technology to Reimagine Freight Invoice Processing

Status quo of electronic invoice processing at municipalities in the federal state of Baden-Württemberg

On 16 April 2014, the European Parliament and the Council of the European Union adopted the Directive on electronic invoicing for public contracts (2014/55/EU) in order to establish electronic invoicing as the predominant method by 2020. The e-invoicing directive requires all contracting authorities to be able to receive and process electronic invoices (ABI L133/1). The European legislator defines e-bill as "an invoice issued, transmitted and received in a structured electronic format that enables its automatic and electronic processing" (ABI L133/7).
 This raises the question of (a) the status quo of electronic invoicing in Baden-Württemberg's municipalities, (b) their implementation barriers and drivers, and (c) the status quo in Baden- Württemberg compared to all other German states. This paper answers these questions and points out interesting aspects that raise new research questions.

Invoice Processing Using Robotic Process Automation

This paper describes our recent effort to develop a automative application to transform invoice processing in Finance operations. As a prime example of the technology’s potential for driving efficiency, Robotic Process Automation (RPA) can be applied to a number of finance and accounting operations, invoice processing. RPA DataBot can automate data input, error reconciliation, and some of the decision-making required by finance staff when processing invoices. At the same time, automation is able to limit errors in such processes and reduce the need for manual exception handling. UiPath’s RPA DataBot are able to constantly monitor a dedicated folder where invoices are saved by employees (or other DataBot) in PDF format. Once robots detect the presence of an invoice in the folder, they begin to extract information from the document. Using intelligent Optical Character Recognition (OCR) and natural language processing capabilities, DataBot are able to read out the information that is visible on the invoice. After robots extract the key information from each invoice, they use their credentials to open the company’s database or enterprise resource planning system, if not already open. The robots then start processing the invoices one-by-one by transferring over the relevant invoice information. During this whole process, the DataBot are also running background activities such as monitoring the dedicated invoice folder or its email address, performing basic checks to see if the company’s database is open, and verifying whether vendor information (e.g. VAT number) on the invoice matches what is already in the database.

The Future Digital Work Force: Robotic Process Automation (RPA)

The Robotic Process Automation (RPA) is a new wave of future technologies. Robotic Process Automation is one of the most advanced technologies in the area of computers science, electronic and communications, mechanical engineering, and information technology. It is a combination of both hardware and software, networking and automation for doing things very simple. In this light, the research manuscript investigated the secondary data - which is available on google, academic and research databases. The investigation went for totally 6 months, i.e., 1-1-2018 to 30-6-2018. A very few empirical articles, white papers, blogs and were found RPA and came across to compose this research manuscript. This study is exploratory in nature because of the contemporary phenomenon. The keywords used in searching of the database were Robotic Process Automation, RPA, Robots, Artificial Intelligence, Blue Prism. The study finally discovered that Robots and Robotic Process Automation technologies are becoming compulsory as a part to do business operations in organizations across the globe. Robotic Process Automation can bring immediate value to the core business processes including employee payroll, employee status changes, new hire recruitment, and onboarding, accounts receivable and payable, invoice processing, inventory management, report creation, software installations, data migration, and vendor onboarding etc. to name a few applications. Besides, the Robotic Process Automation has abundant applications including healthcare and pharmaceuticals, financial services, outsourcing, retail, telecom, energy and utilities, real estate and FMCG and many more sectors. To put in the right place of RPA in business operations, their many allied technologies are working at the background level, artificial intelligence, machine learning, deep learning, data analytics, HR analytics, virtual reality (second life), home automation, blockchain technologies, 4D printing etc. Moreover, it covers the content of different start-ups companies and existing companies - their RPA applications used across the world. This manuscript will be a good guideline for the academicians, researchers, students, and practitioners to get an overall idea.